def test_optuna_search_convert_deprecated_distribution() -> None: param_dist = { "ud": distributions.UniformDistribution(low=0, high=10), "dud": distributions.DiscreteUniformDistribution(low=0, high=10, q=2), "lud": distributions.LogUniformDistribution(low=1, high=10), "id": distributions.IntUniformDistribution(low=0, high=10), "idd": distributions.IntUniformDistribution(low=0, high=10, step=2), "ild": distributions.IntLogUniformDistribution(low=1, high=10), } expected_param_dist = { "ud": distributions.FloatDistribution(low=0, high=10, log=False, step=None), "dud": distributions.FloatDistribution(low=0, high=10, log=False, step=2), "lud": distributions.FloatDistribution(low=1, high=10, log=True, step=None), "id": distributions.IntDistribution(low=0, high=10, log=False, step=1), "idd": distributions.IntDistribution(low=0, high=10, log=False, step=2), "ild": distributions.IntDistribution(low=1, high=10, log=True, step=1), } optuna_search = integration.OptunaSearchCV( KernelDensity(), param_dist, ) assert optuna_search.param_distributions == expected_param_dist # It confirms that ask doesn't convert non-deprecated distributions. optuna_search = integration.OptunaSearchCV( KernelDensity(), expected_param_dist, ) assert optuna_search.param_distributions == expected_param_dist
def test_check_distribution_compatibility(): # type: () -> None # test the same distribution for key in EXAMPLE_JSONS.keys(): distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS[key], EXAMPLE_DISTRIBUTIONS[key]) # test different distribution classes pytest.raises( ValueError, lambda: distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["u"], EXAMPLE_DISTRIBUTIONS["l"]), ) # test dynamic value range (CategoricalDistribution) pytest.raises( ValueError, lambda: distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["c2"], distributions.CategoricalDistribution(choices=("Roppongi", "Akasaka")), ), ) # test dynamic value range (except CategoricalDistribution) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["u"], distributions.UniformDistribution(low=-3.0, high=-2.0)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["l"], distributions.LogUniformDistribution(low=0.1, high=1.0)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["du"], distributions.DiscreteUniformDistribution(low=-1.0, high=11.0, q=3.0), ) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["iu"], distributions.IntUniformDistribution(low=-1, high=1)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["ilu"], distributions.IntLogUniformDistribution(low=1, high=13), )
def test_empty_distribution(): # type: () -> None # Empty distributions cannot be instantiated. with pytest.raises(ValueError): distributions.UniformDistribution(low=0.0, high=-100.0) with pytest.raises(ValueError): distributions.LogUniformDistribution(low=7.3, high=7.2) with pytest.raises(ValueError): distributions.DiscreteUniformDistribution(low=-30, high=-40, q=3) with pytest.raises(ValueError): distributions.IntUniformDistribution(low=123, high=100) with pytest.raises(ValueError): distributions.IntUniformDistribution(low=123, high=100, step=2) with pytest.raises(ValueError): distributions.CategoricalDistribution(choices=())
def test_ask_distribution_conversion() -> None: fixed_distributions = { "ud": distributions.UniformDistribution(low=0, high=10), "dud": distributions.DiscreteUniformDistribution(low=0, high=10, q=2), "lud": distributions.LogUniformDistribution(low=1, high=10), "id": distributions.IntUniformDistribution(low=0, high=10), "idd": distributions.IntUniformDistribution(low=0, high=10, step=2), "ild": distributions.IntLogUniformDistribution(low=1, high=10), } study = create_study() with pytest.warns( FutureWarning, match="See https://github.com/optuna/optuna/issues/2941", ) as record: trial = study.ask(fixed_distributions=fixed_distributions) assert len(record) == 6 expected_distributions = { "ud": distributions.FloatDistribution(low=0, high=10, log=False, step=None), "dud": distributions.FloatDistribution(low=0, high=10, log=False, step=2), "lud": distributions.FloatDistribution(low=1, high=10, log=True, step=None), "id": distributions.IntDistribution(low=0, high=10, log=False, step=1), "idd": distributions.IntDistribution(low=0, high=10, log=False, step=2), "ild": distributions.IntDistribution(low=1, high=10, log=True, step=1), } assert trial.distributions == expected_distributions
def test_empty_range_contains(): # type: () -> None u = distributions.UniformDistribution(low=1.0, high=1.0) assert not u._contains(0.9) assert u._contains(1.0) assert not u._contains(1.1) lu = distributions.LogUniformDistribution(low=1.0, high=1.0) assert not lu._contains(0.9) assert lu._contains(1.0) assert not lu._contains(1.1) du = distributions.DiscreteUniformDistribution(low=1.0, high=1.0, q=2.0) assert not du._contains(0.9) assert du._contains(1.0) assert not du._contains(1.1) iu = distributions.IntUniformDistribution(low=1, high=1) assert not iu._contains(0) assert iu._contains(1) assert not iu._contains(2)
def test_suggest_discrete_uniform(storage_init_func): # type: (typing.Callable[[], storages.BaseStorage]) -> None mock = Mock() mock.side_effect = [1., 2., 3.] sampler = samplers.RandomSampler() with patch.object(sampler, 'sample', mock) as mock_object: study = create_study(storage_init_func(), sampler=sampler) trial = Trial(study, study.storage.create_new_trial_id(study.study_id)) distribution = distributions.DiscreteUniformDistribution(low=0., high=3., q=1.) assert trial._suggest('x', distribution) == 1. # Test suggesting a param. assert trial._suggest( 'x', distribution) == 1. # Test suggesting the same param. assert trial._suggest( 'y', distribution) == 3. # Test suggesting a different param. assert trial.params == {'x': 1., 'y': 3.} assert mock_object.call_count == 3
def suggest_discrete_uniform(self, name, low, high, q): # type: (str, float, float, float) -> float """Suggest a value for the discrete parameter. The value is sampled from the range ``[low, high]``, and the step of discretization is ``q``. Example: Suggest a fraction of samples used for fitting the individual learners of `GradientBoostingClassifier <https://scikit-learn.org/stable/modules/generated/ sklearn.ensemble.GradientBoostingClassifier.html>`_. .. code:: >>> def objective(trial): >>> ... >>> subsample = trial.suggest_discrete_uniform('subsample', 0.1, 1.0, 0.1) >>> clf = sklearn.ensemble.GradientBoostingClassifier(subsample=subsample) >>> ... Args: name: A parameter name. low: Lower endpoint of the range of suggested values. ``low`` is included in the range. high: Upper endpoint of the range of suggested values. ``high`` is included in the range. q: A step of discretization. Returns: A suggested float value. """ discrete = distributions.DiscreteUniformDistribution(low=low, high=high, q=q) return self._suggest(name, discrete)
def test_suggest_discrete_uniform(storage_init_func): # type: (typing.Callable[[], storages.BaseStorage]) -> None mock = Mock() mock.side_effect = [1.0, 2.0, 3.0] sampler = samplers.RandomSampler() with patch.object(sampler, "sample_independent", mock) as mock_object: study = create_study(storage_init_func(), sampler=sampler) trial = Trial(study, study._storage.create_new_trial(study._study_id)) distribution = distributions.DiscreteUniformDistribution(low=0.0, high=3.0, q=1.0) assert trial._suggest("x", distribution) == 1.0 # Test suggesting a param. assert trial._suggest( "x", distribution) == 1.0 # Test suggesting the same param. assert trial._suggest( "y", distribution) == 3.0 # Test suggesting a different param. assert trial.params == {"x": 1.0, "y": 3.0} assert mock_object.call_count == 3
def test_contains(): # type: () -> None u = distributions.UniformDistribution(low=1., high=2.) assert not u._contains(0.9) assert u._contains(1) assert u._contains(1.5) assert not u._contains(2) lu = distributions.LogUniformDistribution(low=0.001, high=100) assert not lu._contains(0.0) assert lu._contains(0.001) assert lu._contains(12.3) assert not lu._contains(100) du = distributions.DiscreteUniformDistribution(low=1., high=10., q=2.) assert not du._contains(0.9) assert du._contains(1.0) assert du._contains(3.5) assert du._contains(6) assert du._contains(10) assert not du._contains(10.1) iu = distributions.IntUniformDistribution(low=1, high=10) assert not iu._contains(0.9) assert iu._contains(1) assert iu._contains(3.5) assert iu._contains(6) assert iu._contains(10) assert iu._contains(10.1) assert not iu._contains(11) c = distributions.CategoricalDistribution(choices=('Roppongi', 'Azabu')) assert not c._contains(-1) assert c._contains(0) assert c._contains(1) assert c._contains(1.5) assert not c._contains(3)
def test_infer_relative_search_space() -> None: sampler = TPESampler() search_space = { "a": distributions.UniformDistribution(1.0, 100.0), "b": distributions.LogUniformDistribution(1.0, 100.0), "c": distributions.DiscreteUniformDistribution(1.0, 100.0, 3.0), "d": distributions.IntUniformDistribution(1, 100), "e": distributions.IntUniformDistribution(0, 100, step=2), "f": distributions.IntLogUniformDistribution(1, 100), "g": distributions.CategoricalDistribution(["x", "y", "z"]), } def obj(t: Trial) -> float: t.suggest_uniform("a", 1.0, 100.0) t.suggest_loguniform("b", 1.0, 100.0) t.suggest_discrete_uniform("c", 1.0, 100.0, 3.0) t.suggest_int("d", 1, 100) t.suggest_int("e", 0, 100, step=2) t.suggest_int("f", 1, 100, log=True) t.suggest_categorical("g", ["x", "y", "z"]) return 0.0 # Study and frozen-trial are not supposed to be accessed. study1 = Mock(spec=[]) frozen_trial = Mock(spec=[]) assert sampler.infer_relative_search_space(study1, frozen_trial) == {} study2 = optuna.create_study(sampler=sampler) study2.optimize(obj, n_trials=1) assert sampler.infer_relative_search_space(study2, study2.best_trial) == {} with warnings.catch_warnings(): warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning) sampler = TPESampler(multivariate=True) study3 = optuna.create_study(sampler=sampler) study3.optimize(obj, n_trials=1) assert sampler.infer_relative_search_space( study3, study3.best_trial) == search_space
def suggest_float(self, name, low, high, *, log=False, step=None): # type: (str, float, float, bool, Optional[float]) -> float if step is not None: if log: raise NotImplementedError( "The parameter `step` is not supported when `log` is True." ) else: return self._suggest( name, distributions.DiscreteUniformDistribution(low=low, high=high, q=step)) else: if log: return self._suggest( name, distributions.LogUniformDistribution(low=low, high=high)) else: return self._suggest( name, distributions.UniformDistribution(low=low, high=high))
def test_convert_old_distribution_to_new_distribution() -> None: ud = distributions.UniformDistribution(low=0, high=10) assert distributions._convert_old_distribution_to_new_distribution( ud) == distributions.FloatDistribution(low=0, high=10, log=False, step=None) dud = distributions.DiscreteUniformDistribution(low=0, high=10, q=2) assert distributions._convert_old_distribution_to_new_distribution( dud) == distributions.FloatDistribution(low=0, high=10, log=False, step=2) lud = distributions.LogUniformDistribution(low=1, high=10) assert distributions._convert_old_distribution_to_new_distribution( lud) == distributions.FloatDistribution(low=1, high=10, log=True, step=None) id = distributions.IntUniformDistribution(low=0, high=10) assert distributions._convert_old_distribution_to_new_distribution( id) == distributions.IntDistribution(low=0, high=10, log=False, step=1) idd = distributions.IntUniformDistribution(low=0, high=10, step=2) assert distributions._convert_old_distribution_to_new_distribution( idd) == distributions.IntDistribution(low=0, high=10, log=False, step=2) ild = distributions.IntLogUniformDistribution(low=1, high=10) assert distributions._convert_old_distribution_to_new_distribution( ild) == distributions.IntDistribution(low=1, high=10, log=True, step=1)
def test_distributions(storage_init_func): # type: (typing.Callable[[], storages.BaseStorage]) -> None def objective(trial): # type: (Trial) -> float trial.suggest_uniform("a", 0, 10) trial.suggest_loguniform("b", 0.1, 10) trial.suggest_discrete_uniform("c", 0, 10, 1) trial.suggest_int("d", 0, 10) trial.suggest_categorical("e", ["foo", "bar", "baz"]) return 1.0 study = create_study(storage_init_func()) study.optimize(objective, n_trials=1) assert study.best_trial.distributions == { "a": distributions.UniformDistribution(low=0, high=10), "b": distributions.LogUniformDistribution(low=0.1, high=10), "c": distributions.DiscreteUniformDistribution(low=0, high=10, q=1), "d": distributions.IntUniformDistribution(low=0, high=10), "e": distributions.CategoricalDistribution(choices=("foo", "bar", "baz")), }
def test_distributions(storage_init_func): # type: (typing.Callable[[], storages.BaseStorage]) -> None def objective(trial): # type: (Trial) -> float trial.suggest_uniform('a', 0, 10) trial.suggest_loguniform('b', 0.1, 10) trial.suggest_discrete_uniform('c', 0, 10, 1) trial.suggest_int('d', 0, 10) trial.suggest_categorical('e', ['foo', 'bar', 'baz']) return 1.0 study = create_study(storage_init_func()) study.optimize(objective, n_trials=1) assert study.best_trial.distributions == { 'a': distributions.UniformDistribution(low=0, high=10), 'b': distributions.LogUniformDistribution(low=0.1, high=10), 'c': distributions.DiscreteUniformDistribution(low=0, high=10, q=1), 'd': distributions.IntUniformDistribution(low=0, high=10), 'e': distributions.CategoricalDistribution(choices=('foo', 'bar', 'baz')) }
def test_single(): # type: () -> None with warnings.catch_warnings(): # UserWarning will be raised since the range is not divisible by step. warnings.simplefilter("ignore", category=UserWarning) single_distributions = [ distributions.UniformDistribution(low=1.0, high=1.0), distributions.LogUniformDistribution(low=7.3, high=7.3), distributions.DiscreteUniformDistribution(low=2.22, high=2.22, q=0.1), distributions.DiscreteUniformDistribution(low=2.22, high=2.24, q=0.3), distributions.IntUniformDistribution(low=-123, high=-123), distributions.IntUniformDistribution(low=-123, high=-120, step=4), distributions.CategoricalDistribution(choices=("foo", )), distributions.IntLogUniformDistribution(low=2, high=2), distributions.IntLogUniformDistribution(low=2, high=2, step=2), ] # type: List[distributions.BaseDistribution] for distribution in single_distributions: assert distribution.single() nonsingle_distributions = [ distributions.UniformDistribution(low=1.0, high=1.001), distributions.LogUniformDistribution(low=7.3, high=10), distributions.DiscreteUniformDistribution(low=-30, high=-20, q=2), distributions.DiscreteUniformDistribution(low=-30, high=-20, q=10), # In Python, "0.3 - 0.2 != 0.1" is True. distributions.DiscreteUniformDistribution(low=0.2, high=0.3, q=0.1), distributions.DiscreteUniformDistribution(low=0.7, high=0.8, q=0.1), distributions.IntUniformDistribution(low=-123, high=0), distributions.IntUniformDistribution(low=-123, high=0, step=123), distributions.CategoricalDistribution(choices=("foo", "bar")), distributions.IntLogUniformDistribution(low=2, high=4), distributions.IntLogUniformDistribution(low=2, high=4, step=2), ] # type: List[distributions.BaseDistribution] for distribution in nonsingle_distributions: assert not distribution.single()
def suggest_discrete_uniform(self, name, low, high, q): # type: (str, float, float, float) -> float high = _adjust_discrete_uniform_high(name, low, high, q) discrete = distributions.DiscreteUniformDistribution(low=low, high=high, q=q) return self._suggest(name, discrete)
def test_contains() -> None: u = distributions.UniformDistribution(low=1.0, high=2.0) assert not u._contains(0.9) assert u._contains(1) assert u._contains(1.5) assert not u._contains(2) lu = distributions.LogUniformDistribution(low=0.001, high=100) assert not lu._contains(0.0) assert lu._contains(0.001) assert lu._contains(12.3) assert not lu._contains(100) with warnings.catch_warnings(): # UserWarning will be raised since the range is not divisible by 2. # The range will be replaced with [1.0, 9.0]. warnings.simplefilter("ignore", category=UserWarning) du = distributions.DiscreteUniformDistribution(low=1.0, high=10.0, q=2.0) assert not du._contains(0.9) assert du._contains(1.0) assert du._contains(3.5) assert du._contains(6) assert du._contains(9) assert not du._contains(9.1) assert not du._contains(10) iu = distributions.IntUniformDistribution(low=1, high=10) assert not iu._contains(0.9) assert iu._contains(1) assert iu._contains(4) assert iu._contains(6) assert iu._contains(10) assert not iu._contains(10.1) assert not iu._contains(11) # IntUniformDistribution with a 'step' parameter. with warnings.catch_warnings(): # UserWarning will be raised since the range is not divisible by 2. # The range will be replaced with [1, 9]. warnings.simplefilter("ignore", category=UserWarning) iuq = distributions.IntUniformDistribution(low=1, high=10, step=2) assert not iuq._contains(0.9) assert iuq._contains(1) assert iuq._contains(4) assert iuq._contains(6) assert iuq._contains(9) assert not iuq._contains(9.1) assert not iuq._contains(10) c = distributions.CategoricalDistribution(choices=("Roppongi", "Azabu")) assert not c._contains(-1) assert c._contains(0) assert c._contains(1) assert c._contains(1.5) assert not c._contains(3) ilu = distributions.IntUniformDistribution(low=2, high=12) assert not ilu._contains(0.9) assert ilu._contains(2) assert ilu._contains(4) assert ilu._contains(6) assert ilu._contains(12) assert not ilu._contains(12.1) assert not ilu._contains(13) iluq = distributions.IntLogUniformDistribution(low=2, high=7) assert not iluq._contains(0.9) assert iluq._contains(2) assert iluq._contains(4) assert iluq._contains(5) assert iluq._contains(6) assert not iluq._contains(7.1) assert not iluq._contains(8)
import copy import json from typing import Any from typing import Dict from typing import List import warnings import pytest from optuna import distributions EXAMPLE_DISTRIBUTIONS = { "u": distributions.UniformDistribution(low=1.0, high=2.0), "l": distributions.LogUniformDistribution(low=0.001, high=100), "du": distributions.DiscreteUniformDistribution(low=1.0, high=9.0, q=2.0), "iu": distributions.IntUniformDistribution(low=1, high=9, step=2), "c1": distributions.CategoricalDistribution(choices=(2.71, -float("inf"))), "c2": distributions.CategoricalDistribution(choices=("Roppongi", "Azabu")), "c3": distributions.CategoricalDistribution(choices=["Roppongi", "Azabu"]), "ilu": distributions.IntLogUniformDistribution(low=2, high=12, step=2), } # type: Dict[str, Any] EXAMPLE_JSONS = { "u": '{"name": "UniformDistribution", "attributes": {"low": 1.0, "high": 2.0}}', "l": '{"name": "LogUniformDistribution", "attributes": {"low": 0.001, "high": 100}}', "du": '{"name": "DiscreteUniformDistribution",' '"attributes": {"low": 1.0, "high": 9.0, "q": 2.0}}', "iu":
def test_check_distribution_compatibility() -> None: # test the same distribution for key in EXAMPLE_JSONS: distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS[key], EXAMPLE_DISTRIBUTIONS[key]) # test different distribution classes pytest.raises( ValueError, lambda: distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["i"], EXAMPLE_DISTRIBUTIONS["fl"]), ) pytest.raises( ValueError, lambda: distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["u"], EXAMPLE_DISTRIBUTIONS["l"]), ) # test compatibility between IntDistributions. distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["i"], EXAMPLE_DISTRIBUTIONS["il"]) distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["il"], EXAMPLE_DISTRIBUTIONS["id"]) distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["id"], EXAMPLE_DISTRIBUTIONS["i"]) # test compatibility between FloatDistributions. distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["f"], EXAMPLE_DISTRIBUTIONS["fl"]) distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["fl"], EXAMPLE_DISTRIBUTIONS["fd"]) distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["fd"], EXAMPLE_DISTRIBUTIONS["f"]) # test dynamic value range (CategoricalDistribution) pytest.raises( ValueError, lambda: distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["c2"], distributions.CategoricalDistribution(choices=("Roppongi", "Akasaka")), ), ) # test dynamic value range (except CategoricalDistribution) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["i"], distributions.IntDistribution(low=-3, high=2)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["il"], distributions.IntDistribution(low=1, high=13, log=True)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["id"], distributions.IntDistribution(low=-3, high=2, step=2)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["f"], distributions.FloatDistribution(low=-3.0, high=-2.0)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["fl"], distributions.FloatDistribution(low=0.1, high=1.0, log=True)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["fd"], distributions.FloatDistribution(low=-1.0, high=11.0, step=0.5)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["u"], distributions.UniformDistribution(low=-3.0, high=-2.0)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["l"], distributions.LogUniformDistribution(low=0.1, high=1.0)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["du"], distributions.DiscreteUniformDistribution(low=-1.0, high=11.0, q=3.0), ) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["iu"], distributions.IntUniformDistribution(low=-1, high=1)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["iuq"], distributions.IntUniformDistribution(low=-1, high=1)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["ilu"], distributions.IntLogUniformDistribution(low=1, high=13)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS["iluq"], distributions.IntLogUniformDistribution(low=1, high=13))
def suggest_discrete_uniform(self, name: str, low: float, high: float, q: float) -> float: discrete = distributions.DiscreteUniformDistribution(low=low, high=high, q=q) return self._suggest(name, discrete)
import json import pytest from optuna import distributions from optuna import type_checking if type_checking.TYPE_CHECKING: from typing import Any # NOQA from typing import Dict # NOQA from typing import List # NOQA EXAMPLE_DISTRIBUTIONS = { 'u': distributions.UniformDistribution(low=1., high=2.), 'l': distributions.LogUniformDistribution(low=0.001, high=100), 'du': distributions.DiscreteUniformDistribution(low=1., high=10., q=2.), 'iu': distributions.IntUniformDistribution(low=1, high=10), 'c1': distributions.CategoricalDistribution(choices=(2.71, -float('inf'))), 'c2': distributions.CategoricalDistribution(choices=('Roppongi', 'Azabu')) } # type: Dict[str, Any] EXAMPLE_JSONS = { 'u': '{"name": "UniformDistribution", "attributes": {"low": 1.0, "high": 2.0}}', 'l': '{"name": "LogUniformDistribution", "attributes": {"low": 0.001, "high": 100}}', 'du': '{"name": "DiscreteUniformDistribution",' '"attributes": {"low": 1.0, "high": 10.0, "q": 2.0}}', 'iu': '{"name": "IntUniformDistribution", "attributes": {"low": 1, "high": 10}}', 'c1': '{"name": "CategoricalDistribution", "attributes": {"choices": [2.71, -Infinity]}}', 'c2': '{"name": "CategoricalDistribution", "attributes": {"choices": ["Roppongi", "Azabu"]}}' }
from typing import Dict from typing import List from unittest.mock import patch import numpy as np import pytest from optuna import distributions from optuna.samplers._tpe.parzen_estimator import _ParzenEstimator from optuna.samplers._tpe.parzen_estimator import _ParzenEstimatorParameters from optuna.samplers._tpe.sampler import default_weights SEARCH_SPACE = { "a": distributions.UniformDistribution(1.0, 100.0), "b": distributions.LogUniformDistribution(1.0, 100.0), "c": distributions.DiscreteUniformDistribution(1.0, 100.0, 3.0), "d": distributions.IntUniformDistribution(1, 100), "e": distributions.IntLogUniformDistribution(1, 100), "f": distributions.CategoricalDistribution(["x", "y", "z"]), } MULTIVARIATE_SAMPLES = { "a": np.array([1.0]), "b": np.array([1.0]), "c": np.array([1.0]), "d": np.array([1]), "e": np.array([1]), "f": np.array([1]), } _PRECOMPUTE_SIGMAS0 = "optuna.samplers._tpe.parzen_estimator._ParzenEstimator._precompute_sigmas0"
def suggest_discrete_uniform(self, name, low, high, q): # type: (str, float, float, float) -> float """Suggest a value for the discrete parameter. The value is sampled from the range :math:`[\\mathsf{low}, \\mathsf{high}]`, and the step of discretization is :math:`q`. More specifically, this method returns one of the values in the sequence :math:`\\mathsf{low}, \\mathsf{low} + q, \\mathsf{low} + 2 q, \\dots, \\mathsf{low} + k q \\le \\mathsf{high}`, where :math:`k` denotes an integer. Note that :math:`high` may be changed due to round-off errors if :math:`q` is not an integer. Please check warning messages to find the changed values. Example: Suggest a fraction of samples used for fitting the individual learners of `GradientBoostingClassifier <https://scikit-learn.org/stable/modules/generated/ sklearn.ensemble.GradientBoostingClassifier.html>`_. .. testsetup:: import numpy as np from sklearn.model_selection import train_test_split np.random.seed(seed=0) X = np.random.randn(50).reshape(-1, 1) y = np.random.randint(0, 2, 50) X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0) .. testcode:: import optuna from sklearn.ensemble import GradientBoostingClassifier def objective(trial): subsample = trial.suggest_discrete_uniform('subsample', 0.1, 1.0, 0.1) clf = GradientBoostingClassifier(subsample=subsample, random_state=0) clf.fit(X_train, y_train) return clf.score(X_valid, y_valid) study = optuna.create_study(direction='maximize') study.optimize(objective, n_trials=3) Args: name: A parameter name. low: Lower endpoint of the range of suggested values. ``low`` is included in the range. high: Upper endpoint of the range of suggested values. ``high`` is included in the range. q: A step of discretization. Returns: A suggested float value. """ high = _adjust_discrete_uniform_high(name, low, high, q) distribution = distributions.DiscreteUniformDistribution(low=low, high=high, q=q) self._check_distribution(name, distribution) if low == high: return self._set_new_param_or_get_existing(name, low, distribution) return self._suggest(name, distribution)